Using fuzzy logic to renewable energy forecasting: a case study of France

Mounir Ben Mbarek and Rochdi Feki

International Journal of Energy Technology and Policy, 2016, vol. 12, issue 4, 357-376

Abstract: The orientation toward renewable energy consumption has led to decrease pollutants such as greenhouse gases that affect human health, natural ecosystems, agriculture, and earth temperature. Despite the growing literature on renewable energy sources and its relationship with other energetic and economic variables, precise estimation and forecasting renewable energy is crucial for the policy and decision-making process in the energy and economic sector. This study presents a new fuzzy logic approach to forecast renewable energy production in France on the basis of quarterly data from 2001Q1 to 2012Q3. The result of the proposed approach has been compared to that of the vector autoregressive (VAR) model and a neural network modelling, such as NARX approach.

Keywords: renewable energy forecasting; fuzzy logic; artificial neural networks; ANNs; VAR model; case study; France; modelling. (search for similar items in EconPapers)
Date: 2016
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